Reinforcement learning 19 Colaboratory + Mountain_car + ChainerRL

It is assumed that you have completed reinforcement learning 18. It continued to be cut off on the way. Feeling that it is forcibly terminated. Therefore, I ended every 2000 epochs and continued it if there was previous data. If you do it in runtime mode without GPU, it may be cut off due to Google's convenience like preemptible. It was okay in GPU runtime mode. Is that so? It has been reached after 4000 learning sessions.

Google Drive mount

import google.colab.drive
google.colab.drive.mount('gdrive')
!ln -s gdrive/My\ Drive mydrive

Installation

!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1
!pip install pyvirtualdisplay > /dev/null 2>&1
!pip -q install JSAnimation
!pip -q install chainerrl

Parameter initialization

gamename='MountainCar-v0'
# Set the discount factor that discounts future rewards.
gamma = 0.99
# Use epsilon-greedy for exploration
myepsilon=0.003
myDir='mydrive/OpenAI/MountainCar/'
mySteps=400000 # Train the agent for 2000 steps
my_eval_n_episodes=1 # 10 episodes are sampled for each evaluation
my_eval_max_episode_len=200  # Maximum length of each episodes
my_eval_interval=200   # Evaluate the agent after every 1000 steps
myOutDir=myDir+'result'      # Save everything to 'result' directory
myAgentDir=myDir+'agent'      # Save Agent to 'agent' directory
myAnimName=myDir+'movie_montaincar.mp4'
myScoreName=myDir+"result/scores.txt"

Program

import

import chainer
import chainer.functions as F
import chainer.links as L
import chainerrl
import gym
import numpy as np

env initialize

env = gym.make(gamename)
print('observation space:', env.observation_space)
print('action space:', env.action_space)

obs = env.reset()
print('initial observation:', obs)
action = env.action_space.sample()
obs, r, done, info = env.step(action)
print('next observation:', obs)
print('reward:', r)
print('done:', done)
print('info:', info)

Deep Q Network setting

obs_size = env.observation_space.shape[0]
n_actions = env.action_space.n
q_func = chainerrl.q_functions.FCStateQFunctionWithDiscreteAction(
    obs_size, n_actions,
    n_hidden_layers=2, n_hidden_channels=50)

Use Adam to optimize q_func. eps=1e-2 is for stability.

optimizer = chainer.optimizers.Adam(eps=1e-2)
optimizer.setup(q_func)

Agent Setting DQN uses Experience Replay.

Specify a replay buffer and its capacity.

Since observations from CartPole-v0 is numpy.float64 while

Chainer only accepts numpy.float32 by default, specify a converter as a feature extractor function phi.

explorer = chainerrl.explorers.ConstantEpsilonGreedy(
    epsilon=myepsilon, random_action_func=env.action_space.sample)
replay_buffer = chainerrl.replay_buffer.ReplayBuffer(capacity=10 ** 6)
phi = lambda x: x.astype(np.float32, copy=False)
agent = chainerrl.agents.DoubleDQN(
    q_func, optimizer, replay_buffer, gamma, explorer,
    replay_start_size=500, update_interval=1,
    target_update_interval=100, phi=phi)

Train

Set up the logger to print info messages for understandability.

import os
if (os.path.exists(myAgentDir)):
  agent.load(myAgentDir)
import logging
import sys
logging.basicConfig(level=logging.INFO, stream=sys.stdout, format='')
chainerrl.experiments.train_agent_with_evaluation(
    agent, env,steps=mySteps,eval_n_steps=None,eval_n_episodes=my_eval_n_episodes,eval_max_episode_len=my_eval_max_episode_len,
    eval_interval=my_eval_interval,outdir=myOutDir)
agent.save(myAgentDir)

Data Table

import pandas as pd
import glob
import os
score_files = glob.glob(myScoreName)
score_files.sort(key=os.path.getmtime)
score_file = score_files[-1]
df = pd.read_csv(score_file, delimiter='\t' )
df

figure Average_Q

df.plot(x='steps',y='average_q')

Test

import2

from pyvirtualdisplay import Display
display = Display(visible=0, size=(1024, 768))
display.start()


from JSAnimation.IPython_display import display_animation
from matplotlib import animation
import matplotlib.pyplot as plt
%matplotlib inline

Test Program

frames = []
env = gym.make(gamename)
envw = gym.wrappers.Monitor(env, myOutDir, force=True)

for i in range(3):
    obs = envw.reset()
    done = False
    R = 0
    t = 0
    while not done and t < 200:
        frames.append(envw.render(mode = 'rgb_array'))
        action = agent.act(obs)
        obs, r, done, _ = envw.step(action)
        R += r
        t += 1
    print('test episode:', i, 'R:', R)
    agent.stop_episode()
#envw.render()
envw.close()

from IPython.display import HTML
plt.figure(figsize=(frames[0].shape[1]/72.0, frames[0].shape[0]/72.0),dpi=72)
patch = plt.imshow(frames[0])
plt.axis('off') 
def animate(i):
    patch.set_data(frames[i])
anim = animation.FuncAnimation(plt.gcf(), animate, frames=len(frames),interval=50)
anim.save(myAnimName)
HTML(anim.to_jshtml())